A glutamine metabolish-associated prognostic model to predict prognosis and therapeutic responses of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) ranks among the most lethal malignancies around the world. However, the current management strategies for predicting prognosis in HCC patients remain unreliable. Our study developed a robust prognostic model based on glutamine metabolism associated-genes (GMAGs), utilizing data from The Cancer Genome Atlas database. The prognostic values of model were validated through the databases of the Gene Expression Omnibus and International Cancer Genome Consortium via Kaplan‒Meier curves and receiver operating characteristic (ROC). The potential biological pathways associated with prognostic risk were investigated through different enrichment analysis, and Gene variation analysis. The correlation between prognostic model and therapeutic responses were analyzed. Quantitative real-time PCR (qRT-PCR) and cellular experiments were measured to analyze the GMAGs. Consequently, a prognostic model was constructed of 4 GMAGs (RRM1, RRM2, G6PD, and GPX7) through least absolute shrinkage and selection operator (LASSO) regression analysis. The Kaplan‒Meier curves and ROC curves showed a reliable predictive capacity of prognosis for HCC patients (p